High-Performance Array Operations with Cython | Set 1

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Although libraries like NumPy can perform high-performance array processing functions to operate on arrays. But Cython can also work really well. But how ?

Code #1 : Cython function for clipping the values in a simple 1D array of doubles

# work.pyx (Cython file) 
cimport cython 
  
@cython.boundscheck(False) 
@cython.wraparound(False) 
  
cpdef clip(double[:] a, double min, double max, double[:] out): 
  
    ''' 
    Clip the values in a to be between  
    min and max. Result in out 
    '''
    if min > max: 
        raise ValueError("min must be <= max") 
     
    if a.shape[0] != out.shape[0]: 
        raise ValueError("input and output arrays must be the same size") 
      
    for i in range(a.shape[0]): 
        if a[i] < min: 
            out[i] = min
        elif a[i] > max: 
            out[i] = max
        else: 
            out[i] = a[i] 

work.py file is required to compile and build the extension.

Code #2 :

# importing libraries 
from distutils.core import setup 
from distutils.extension import Extension 
from Cython.Distutils import build_ext 
  
ext_modules = [Extension( 
        'sample',  
        ['sample.pyx'])] 
  
setup(name = 'Sample app',  
      cmdclass = {'build_ext': build_ext},  
      ext_modules = ext_modules) 

After performing the task above, now we can check the working of resulting function clips arrays, with many different kinds of array objects.

Code #3 : Working of Clipping Array.

# array module example 
import work 
import array 
import numpy 
  
arr = array.array('d', [1, -3, 4, 7, 2, 0]) 
print ("Array : ", arr) 
  
# Clipping the array 
work.clip(arr, 1, 4, arr) 
print ("\nClipping array : ", arr) 
  
# numpy example 
arr2 = numpy.random.uniform(-10, 10, size = 1000000) 
print ("\narr2 : \n", arr2) 
  
arr3 = numpy.zeros_like(arr2) 
print ("\narr3 : \n", arr3) 
  
work.clip(arr2, -5, 5, arr3) 
print ("\nClipping arr3 : \n", ar3) 
print ("\nMinimum in arr3 : ", min(arr3)) 
print ("\nMaximum in arr3 : ", min(arr3)) 

Output :

Array : array('d', [1.0, -3.0, 4.0, 7.0, 2.0, 0.0])

Clipping array : array('d', [1.0, 1.0, 4.0, 4.0, 2.0, 1.0])

arr2 : 
[-9.55546017, 7.45599334, 0.69248932, ..., 0.69583148, -3.86290931, 2.37266888]

arr3 : array([ 0., 0., 0., ..., 0., 0., 0.])

Clipping arr3 : 
[-5., 5., 0.69248932, ..., 0.69583148, -3.86290931, 2.37266888]

Minimum in arr3 : 5.0

Maximum in arr3 : 5.0